Multi-agent reinforcement learning with cooperation based on eligibility traces  

作　　者：杨玉君 程君实 陈佳品 

机构地区：[1]Information Storage Research Center, Shanghai Jiaotong University, Shanghai 200030, China

出　　处：《Journal of Harbin Institute of Technology(New Series)》2004年第5期564-568,共5页哈尔滨工业大学学报（英文版）

基　　金：SponsoredbytheNationalNaturalScienceFoundationofChina(GrantNo .698890 50 ) .

摘　　要：The application of reinforcement learning is widely used by multi-agent systems in recent years. An agent uses a multi-agent system to cooperate with other agents to accomplish the given task, and one agent′s behavior usually affects the others′ behaviors. In traditional reinforcement learning, one agent takes the others location, so it is difficult to consider the others′ behavior, which decreases the learning efficiency. This paper proposes multi-agent reinforcement learning with cooperation based on eligibility traces, i.e. one agent estimates the other agent′s behavior with the other agent′s eligibility traces. The results of this simulation prove the validity of the proposed learning method.The application of reinforcement learning is widely used by multi-agent systems in recent years. An agent uses a multi-agent system to cooperate with other agents to accomplish the given task, and one agent′s behavior usually affects the others′ behaviors. In traditional reinforcement learning, one agent takes the others location, so it is difficult to consider the others′ behavior, which decreases the learning efficiency. This paper proposes multi-agent reinforcement learning with cooperation based on eligibility traces, i.e. one agent estimates the other agent′s behavior with the other agent′s eligibility traces. The results of this simulation prove the validity of the proposed learning method.

关 键 词：reinforcement learning MULTI-AGENT BEHAVIOR eligibility trace 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]